Fields of application of classification methods in the medical area are related to the division of patients into groups with different diagnoses and drug tolerances. Another application is, for example, traffic engineering, in which sensor measurements are classified into different categories. Classification methods are further used in industrial automation, so as to classify for example product quality to be expected based on sensor values of the industrial process.
Numerous mathematic classification methods are known for processing input information, e.g. automatic learning methods with so-called “Support Vector Machines”. Here, characteristics are first extracted from the input information, which can respectively occur in a certain characteristics value. A certain property of the input information is to be understood as a characteristic. Characteristic value is to be understood as whether, to what extent, or in which manner a certain characteristic is entered into the input information. The value can hereby give only the presence or the non-presence of a characteristic, but the value can also describe arbitrary intermediate steps. In the area of voice processing, a characteristic could for example indicate if information was cut (clipping) or not during the digitalization of an acoustic voice signal. In the area of image processing, a characteristic could indicate a grey tone distribution of pixels of an image. The value can hereby indicate e.g. for every one of 256 grey scale values, how often it occurs. Further characteristics could be the sound volume of a voice signal, the volatility of a share price, the speed of a vehicle, the unevenness of a surface, and the structures of an X-ray image. The examples given show that the extraction of characteristics is used in diverse areas of data processing.
Within the scope of the known mathematical methods, a classification of the extracting characteristics takes place after the extraction of different characteristics of the input information. If edges in an image are extracted as characteristics, it can be classified in a second step if the edges belong for example to the image of a face or a building. It is hereby disadvantageous that most methods cannot themselves decide which characteristics are important for the later classification and which are unimportant. Such a discrimination of characteristics in view of a task to be solved has then to take place by hand and has to be given to the system in any form. Finally, methods are also known which can choose characteristics selectively. However, the extraction of the characteristics or their value remains unaffected thereby.
From specification [1] is known a neural network which permits a selective representation of the value of characteristics of input information as a function of an attention filter. A characteristic is hereby the location of an object, which occurs in the values on the left and on the right; another characteristic is the type of the object, which occurs in the values “target object” and “other object”. The representation of the values of these characteristics is selectively influenced by an attention filter.
By the representation of the values of the characteristics, it will be possible to strengthen, filter, mask, differentiate, emphasize, weight and evaluate certain characteristics or their value. This takes place by weighting the individual values of the characteristics in the representation. If, for example, a characteristic “grey value” only occurs in the values “black” and “white”, a deep black input information can be represented by imparting a particularly high weight to the value “black” compared to other characteristics. In the specification [1], such a large weight of a value is represented by a pool of neurons with high activity.
It is however again disadvantageous here that the attention filter, that is, the information about the relevance of the individual characteristics, has to be fed by hand from the outside. Here, it is not possible to generate the neural network in an automated manner as a function of the relevance of the characteristics.
The document [Richard P. Lippmann: An Introduction to Computing with Neural Nets, IEEE ASSP MAGAZINE APRIL 1987, p. 4-22] relates to a general introduction into the calculation methods of neural networks. The use of neural networks for classifying patterns is also mentioned in the article. Nevertheless, a reward-based learning rule cannot be taken from this specification. In particular, the characteristic, that forwardly- and backwardly-directed weights are strengthened or weakened as a function thereof, if a correct categorizing of input information has taken place previously, is not shown in this document.
The specification [Michael Esslinger and Ingo Schaal: OCR mit SNNS, Mustererkennung mit neuronalen Netzen, Praktikumsbericht zum Vortrag Künstliche Intelligenz SS 2004 [OCR with SNNS, pattern recognition with neural networks, internship report for the presentation of artificial intelligence SS 2004] dated Feb. 7, 2004, 16 pages] also concerns the pattern recognition in neural networks. In the specification are also described several learning rules in paragraph 4, but where the adaptation of the weights does not take place in the manner as is established according to the invention. The specification [Siegfried Macho: Modelle des Lernens: Neuronale Netze, [Learning model: neural networks] Universitas Friburgensis, May 93, 6 pages] also relates to a general article regarding learning models with neural networks. In this article, the adaptation of associated connections is mentioned, but in this document there is also no indication of the special reward-based Hebb's learning method according to the invention.